# uniform

uniform, a Python code which returns a sequence of uniformly distributed pseudorandom numbers.

The fundamental underlying random number generator is based on a simple, old, and limited linear congruential random number generator originally used in the IBM System 360. If you want state of the art random number generation, look elsewhere!

Python has the Random.random() function, which returns a random real number in the unit interval.

However, the UNIFORM code makes it possible to compare certain computations that use uniform random numbers, written in C, C++, FORTRAN77, FORTRAN90, Mathematica, MATLAB or Python.

Various types of random data can be computed. The routine names are chosen to indicate the corresponding type:

• B, an integer binary value of 0 or 1.
• C4, complex ( single precision )
• C8, complex ( double precision )
• CH, character
• I4, integer ( single precision )
• L4, logical
• R4, real ( single precision )
• R8, real ( double precision )

In some cases, a one dimension vector or two dimensional array of values is to be generated, and part of the name will therefore include:

• VEC, vector;
• MAT, a matrix of data;

The underlying random numbers are generally defined over some unit interval or region. Routines are available which return these "unit" values, while other routines allow the user to specify limits between which the unit values are rescaled. The name of a routine will usually include a tag suggestig which is the case:

• 01, the data lies in a nit interval or region;
• AB, the data lies in a scaled interval or region;

The random number generator embodied here is not very sophisticated. It will not have the best properties of distribution, noncorrelation and long period. It is not the purpose of this code to achieve such worthy goals. This is simply a reasonably portable code that can be implemented in various languages, on various machines, and for which it is possible, for instance, to regard the output as a function of the seed, and moreover, to work directly with the sequence of seeds, if necessary.

### Languages:

uniform is available in a C version and a C++ version and a FORTRAN90 version and a MATLAB version and a Python version.

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### Reference:

1. Paul Bratley, Bennett Fox, Linus Schrage,
A Guide to Simulation,
Second Edition,
Springer, 1987,
ISBN: 0387964673,
LC: QA76.9.C65.B73.
2. Bennett Fox,
Algorithm 647: Implementation and Relative Efficiency of Quasirandom Sequence Generators,
ACM Transactions on Mathematical Software,
Volume 12, Number 4, December 1986, pages 362-376.
3. Donald Knuth,
The Art of Computer Programming,
Volume 2, Seminumerical Algorithms,
Third Edition,
ISBN: 0201896842,
LC: QA76.6.K64.
4. Pierre LEcuyer,
Random Number Generation,
in Handbook of Simulation,
edited by Jerry Banks,
Wiley, 1998,
ISBN: 0471134031,
LC: T57.62.H37.
5. Peter Lewis, Allen Goodman, James Miller,
A Pseudo-Random Number Generator for the System/360,
IBM Systems Journal,
Volume 8, Number 2, 1969, pages 136-143.
6. Stephen Park, Keith Miller,
Random Number Generators: Good Ones are Hard to Find,
Communications of the ACM,
Volume 31, Number 10, October 1988, pages 1192-1201.
7. Eric Weisstein,
CRC Concise Encyclopedia of Mathematics,
CRC Press, 2002,
Second edition,
ISBN: 1584883472,
LC: QA5.W45.
8. Barry Wilkinson, Michael Allen,
Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers,
Prentice Hall,
ISBN: 0-13-140563-2,
LC: QA76.642.W54.

### Source Code:

Last revised on 06 December 2014.